Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization

Jiho Noh, Ramakanth Kavuluru


Abstract
Information retrieval (IR) for precision medicine (PM) often involves looking for multiple pieces of evidence that characterize a patient case. This typically includes at least the name of a condition and a genetic variation that applies to the patient. Other factors such as demographic attributes, comorbidities, and social determinants may also be pertinent. As such, the retrieval problem is often formulated as ad hoc search but with multiple facets (e.g., disease, mutation) that may need to be incorporated. In this paper, we present a document reranking approach that combines neural query-document matching and text summarization toward such retrieval scenarios. Our architecture builds on the basic BERT model with three specific components for reranking: (a). document-query matching (b). keyword extraction and (c). facet-conditioned abstractive summarization. The outcomes of (b) and (c) are used to essentially transform a candidate document into a concise summary that can be compared with the query at hand to compute a relevance score. Component (a) directly generates a matching score of a candidate document for a query. The full architecture benefits from the complementary potential of document-query matching and the novel document transformation approach based on summarization along PM facets. Evaluations using NIST’s TREC-PM track datasets (2017–2019) show that our model achieves state-of-the-art performance. To foster reproducibility, our code is made available here: https://github.com/bionlproc/text-summ-for-doc-retrieval.
Anthology ID:
2020.findings-emnlp.304
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3389–3399
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.304
DOI:
10.18653/v1/2020.findings-emnlp.304
Bibkey:
Cite (ACL):
Jiho Noh and Ramakanth Kavuluru. 2020. Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3389–3399, Online. Association for Computational Linguistics.
Cite (Informal):
Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization (Noh & Kavuluru, Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.304.pdf
Video:
 https://slideslive.com/38940722
Code
 bionlproc/text-summ-for-doc-retrieval